半监督转移学习用于评估模型分类性能
Linshanshan Wang1, Xuan Wang2, Katherine P Liao3
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States.
Biometrics
|March 11, 2024
概括
本研究引入了准确度测量的半监督转移学习 (STEAM),以评估机器学习模型在没有标签的新数据上的性能. STEAM提高了准确性,并减少了转移学习场景中的偏见.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 生物统计学 生物统计学
背景情况:
- 转移学习面临的挑战是调整模型以适应新的数据分布.
- 在未标记的目标人群上评估模型性能是很困难的.
- 现有的方法缺乏强大的转移性能指标,如ROC参数.
研究的目的:
- 通过ROC分析评估训练有素的二进制分类器在未标记的目标数据上的性能.
- 为机器学习中转移性能指标提出一种高效的方法.
- 解决转移学习环境中强有力的评估需求.
主要方法:
- 拟议的准确度测量的半监督转移学习 (STEAM),一个三步程序.
- 用于校准密度比重的双指数建模.
- 利用强大的归算来利用未标记的数据提高效率.
主要成果:
- 拟议估计器的确定的一致性和异常正常性.
- 用有限样本的交叉验证纠正过拟合偏差.
- 通过模拟与现有方法相比,证明了偏差的减少和效率的提高.
结论:
- STEAM提供了一种高效,强大的方法来评估未标记数据上的分类器性能.
- 该方法适用于现实场景,例如评估电子健康记录中的表型模型.
- 通过实现准确的绩效指标转移,STEAM提高了转移学习的可靠性.
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